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Modeling Assertions: Symbolic Model Representation of Application Performance. Jeffrey S. Vetter (PI) Sadaf R. Alam, Nikhil Bhatia Future Technologies Group Computer Science and Mathematics Division. Modeling assertions. What?

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modeling assertions symbolic model representation of application performance

Modeling Assertions: Symbolic Model Representation of Application Performance

Jeffrey S. Vetter (PI)

Sadaf R. Alam, Nikhil Bhatia

Future Technologies Group Computer Science and Mathematics Division

modeling assertions
Modeling assertions
  • What?
    • Portable and extensible workload and performance modeling framework
  • Why?
    • Provide an integrated solution to develop, validate, and experiment with symbolic performance models of large-scale applications
    • Provide an efficient mechanism for sensitivity analysis and workload project growth rates for future problem and system configurations
  • Components
    • A portable API for code annotation
    • Post-processing tools
    • Synthetic MPI trace generation with OTF compatible format

2 Vetter_Modeling_SC07

modeling workflow

PAP

I

PMPI

MPI task

MPI task

MPI task

MPI task

MPI task

app

MPI task

MPI task

pomp

MPI task

MPI task

MPI task

MPI task

MPI task

MPI task

MPI task

MPI task

MPI task

Modeling workflow

Build

Execute

Application

instrumentation

Symbolic

model

Validate

offline/online

Model

validation

Control

flow

model

Model generation

C-style

model

toward an integrated framework

PAP

I

PMPI

app

pomp

Toward an integrated framework
  • Online validation, analysis and prediction
  • Pragma-based instrumentation
  • Realtime, efficient error reporting mechanisms

Build

Model

validation

Application

instrumentation

Symbolic

model

Control

flow

model

Execute

Validate

Analyze

C-style

model

Report

statistics

sensitivity analysis of doe applications
Sensitivity analysis of DOE applications

Goal: Identify workload sensitivity at scale before system and application deployment

Performance and scaling bottlenecks due to

  • Application parameters
  • System parameters

Message size distribution in the key calculation phases as the MPI grid topology is scaled

modeling on emerging architectures
Modeling on emerging architectures

Goal: Investigate how performance-enhancing features of emerging architecture benefit scientific calculations

Incorporate “application aware” and “architecture aware” parameter in the model

Tv = Tvm+ Tvc

Ts = Tsm+ Tsc

VFLOPS

4.5GHz/18GHz

SFLOPS

0.565GHz/2.26GHz

Tvc =

Tsc =

(VLOADS + VSTORES )*8*64

BW *109* AVL

(SLOADS + SSTORES )*8

BW *109

Tvm =

Tsm =

Vector LS OPS

Vector FP OPS

MVScore

AVL

23. call maf_vec_loop_start(1,“tzetar”,“size^2*(size-1)*26”, (size**2)*(size-1)*26,” (size^2)*(size-1)*16”, (size**2)*(size-1)*16,”(size-1)/(size/64+1)”, (size-1)/(size/64+1),3)

24. M-------< do k = start(3,c), cell_size(3,c)-end(3,c)-1

25. M 2-----< do j = start(2,c), cell_size(2,c)-end(2,c)-1

26. M 2 Vs--< do i = start(1,c), cell_size(1,c)-end(1,c)-1

27. M 2 Vs

performance projections

AVL

V FP LS

V FP OPS

Memory Bandwidth

350

300

250

200

150

100

50

0

-50

-100

Minimum

Average

Maximum

Error (%)

W (1) A (1) B (1) C (1) W (4) A (4) B (4) C (4)

Problem class (MPI Tasks)

Performance projections

8

6

4

2

0

-2

-4

-6

-8

-10

-12

-14

  • Average vector length prediction with different
    • Problem configurations
    • MPI task

Error (%)

W (1) A (1) B (1) C (1) W (4) A (4) B (4) C (4)

Problem class (MPI Tasks)

  • Experimented with memory bandwidth values: maximum (stream), minimum (random) and mean of max and min
  • Performance prediction
synthetic trace file generation
Synthetic trace file generation

Goal: Generate input traces for future problem and system configurations that do not exist

  • Prototype for future network design at scale infeasible
    • Issues:
      • Realistic and representative workload for petascale and exascale systems.
      • Problem configurations; for example, input decks for petascale and exascale problems do not exist.
  • Solution
    • Generate parameterized network models and traces to drive network simulators.
    • Support common MPI trace file formats like OTF.
    • Identify workload scaling behavior and patterns.
    • Investigate alternate algorithms and implementation.
workload sensitivity results

20 K atoms

200 K atoms

2 M atoms

Memory

Message size

Message count

Memory

Message size

Message count

Memory

Message size

Message count

1.E+09

1.E+08

1.E+07

1.E+06

1.E+05

1.E+04

1.E+03

1.E+02

1.E+01

1.E+00

8

1

2

4

16

32

64

128

256

512

8192

2048

4096

1024

65536

16384

32768

524288

131072

262144

1048576

Workload sensitivity results

Number of cores

Sensitivity analysis for workload requirements of the PME implementation in SANDER for petaflops scale systems

contacts
Contacts

10 Vetter_Modeling_SC07

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